Table contains total population and population density summarized at county, city, zip code, and census tract level. Population density is defined as number of people residing per square mile of area. Data are presented for zip codes (ZCTAs) fully within the county. Source: U.S. Census Bureau, 2016-2020 American Community Survey 5-year estimates, Table B01001; data accessed on April 11, 2022 from https://api.census.gov. The 2020 Decennial geographies are used for data summarization.METADATA:notes (String): Lists table title, notes, sourcesgeolevel (String): Level of geographyGEOID (String): Geography IDNAME (String): Name of geographyt_pop (Numeric): Total populationpop_density (Numeric): Area in square milesarea (Numeric): Population density

These datasets contain measures of socioeconomic and demographic characteristics by U.S. census tract for the years 1990-2022 and ZIP code tabulation area (ZCTA) for the years 2008-2022. Example measures include population density; population distribution by race, ethnicity, age, and income; income inequality by race and ethnicity; and proportion of population living below the poverty level, receiving public assistance, and female-headed or single parent families with kids. The datasets also contain a set of theoretically derived measures capturing neighborhood socioeconomic disadvantage and affluence, as well as a neighborhood index of Hispanic, foreign born, and limited English.

A global database of Census Data that provides an understanding of population distribution at administrative and zip code levels over 55 years, past, present, and future.

Leverage up-to-date census data with population trends for real estate, market research, audience targeting, and sales territory mapping.

Self-hosted commercial demographic dataset curated based on trusted sources such as the United Nations or the European Commission, with a 99% match accuracy. The global Census Data is standardized, unified, and ready to use.

Use cases for the Global Census Database (Consumer Demographic Data)

• Ad targeting

• B2B Market Intelligence

• Customer analytics

• Real Estate Data Estimations

• Marketing campaign analysis

• Demand forecasting

• Sales territory mapping

• Retail site selection

• Reporting

• Audience targeting

Census data export methodology

Our consumer demographic data packages are offered in CSV format. All Demographic data are optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more.

Product Features

• Historical population data (55 years)

• Changes in population density

• Urbanization Patterns

• Accurate at zip code and administrative level

• Optimized for easy integration

• Easy customization

• Global coverage

• Updated yearly

• Standardized and reliable

• Self-hosted delivery

• Fully aggregated (ready to use)

• Rich attributes

Why do companies choose our demographic databases

• Standardized and unified demographic data structure

• Seamless integration in your system

• Dedicated location data expert

Note: Custom population data packages are available. Please submit a request via the above contact button for more details.

A global database of population segmentation data that provides an understanding of population distribution at administrative and zip code levels over 55 years, past, present, and future.

Leverage up-to-date audience targeting data trends for market research, audience targeting, and sales territory mapping.

Self-hosted consumer data curated based on trusted sources such as the United Nations or the European Commission, with a 99% match accuracy. The Consumer Data is standardized, unified, and ready to use.

Use cases for the Global Population Database (Consumer Data Data/Segmentation data)

• Ad targeting

• B2B Market Intelligence

• Customer analytics

• Marketing campaign analysis

• Demand forecasting

• Sales territory mapping

• Retail site selection

• Reporting

• Audience targeting

Segmentation data export methodology

Our location data packages are offered in variable formats, including GeoJSON, KML, and TopoJSON. All geospatial data are optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more.

Product Features

• Population density

• Accurate at any level of granularity

• Global coverage

• Updated yearly

• Data spans over 55 years

• Standardized and reliable

• Self-hosted

• Fully aggregated (ready to use)

• Rich attributes

Why do companies choose our Population Databases

• Standardized and unified demographic data structure

• Reduce integration time and cost by 30%

• Dedicated customer success manager

Note: Custom population data packages are available. Please submit a request via the above contact button for more details.

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau across all standard and custom geographies at statewide summary level where applicable. For a deep dive into the data model including every specific metric, see the ACS 2017-2021 Data Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics. Find naming convention prefixes/suffixes, geography definitions and user notes below.Prefixes:NoneCountpPercentrRatemMedianaMean (average)tAggregate (total)chChange in absolute terms (value in t2 - value in t1)pchPercent change ((value in t2 - value in t1) / value in t1)chpChange in percent (percent in t2 - percent in t1)sSignificance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computedSuffixes:_e21Estimate from 2017-21 ACS_m21Margin of Error from 2017-21 ACS_e102006-10 ACS, re-estimated to 2020 geography_m10Margin of Error from 2006-10 ACS, re-estimated to 2020 geography_e10_21Change, 2010-21 (holding constant at 2020 geography)GeographiesAAA = Area Agency on Aging (12 geographic units formed from counties providing statewide coverage)ARC21 = Atlanta Regional Commission modeling area (21 counties merged to a single geographic unit)ARWDB7 = Atlanta Regional Workforce Development Board (7 counties merged to a single geographic unit)BeltLine (buffer)BeltLine Study (subareas)Census Tract (statewide)CFGA23 = Community Foundation for Greater Atlanta (23 counties merged to a single geographic unit)City (statewide)City of Atlanta Council Districts (City of Atlanta)City of Atlanta Neighborhood Planning Unit (City of Atlanta)City of Atlanta Neighborhood Planning Unit STV (3 NPUs merged to a single geographic unit within City of Atlanta)City of Atlanta Neighborhood Statistical Areas (City of Atlanta)City of Atlanta Neighborhood Statistical Areas E02E06 (2 NSAs merged to single geographic unit within City of Atlanta)County (statewide)Georgia House (statewide)Georgia Senate (statewide)MetroWater15 = Atlanta Metropolitan Water District (15 counties merged to a single geographic unit)Regional Commissions (statewide)SPARCC = Strong, Prosperous And Resilient Communities ChallengeState of Georgia (single geographic unit)Superdistrict (ARC region)US Congress (statewide)UWGA13 = United Way of Greater Atlanta (13 counties merged to a single geographic unit)WFF = Westside Future Fund (subarea of City of Atlanta)ZIP Code Tabulation Areas (statewide)The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2017-2021). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2017-2021Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the data manifest: https://garc.maps.arcgis.com/sharing/rest/content/items/34b9adfdcc294788ba9c70bf433bd4c1/data

The Sprawl Score was calculated for Canadian census tracts in 2011 and 2016 by Drs. Henry Luan and Dan Fuller. The score is based on indicators of urban form in four categories - density (population density, gross employment density); centering ( variation in dissemination area populations within census tracts, variation in dissemination area employment density within census tracts); land use (land use mix); and street accessibility (mean land area of dissemination areas within census tracts, percentage of small dissemination areas within census tracts, intersection density, and percentage of 4-or-more way street intersection with census tracts). Bayesian spatial factor analysis was used to combine all indicators into a standardized score. ArcGIS was used by CANUE staff to associate single link DMTI Spatial postal codes to the sprawl score data. All postal codes within a single census tract are assigned the same sprawl score values.

This issue describes in detail the health region limits as of October 2011 and their correspondence with the 2006 and 2001 Census geography. Health regions are defined by the provinces and represent administrative areas or regions of interest to health authorities. This product contains correspondence files (linking health regions to census geographic codes) and digital boundary files. User documentation provides an overview of health regions, sources, methods, limitations and product description (file format and layout). In addition to the geographic files, this product also includes 2006 Census data (basic profile) for health regions. For current Health Regions data, refer to Statistics Canada.

A dataset listing North Carolina zip codes by population for 2024.

IntroductionClimate Central’s Surging Seas: Risk Zone map shows areas vulnerable to near-term flooding from different combinations of sea level rise, storm surge, tides, and tsunamis, or to permanent submersion by long-term sea level rise. Within the U.S., it incorporates the latest, high-resolution, high-accuracy lidar elevation data supplied by NOAA (exceptions: see Sources), displays points of interest, and contains layers displaying social vulnerability, population density, and property value. Outside the U.S., it utilizes satellite-based elevation data from NASA in some locations, and Climate Central’s more accurate CoastalDEM in others (see Methods and Qualifiers). It provides the ability to search by location name or postal code.The accompanying Risk Finder is an interactive data toolkit available for some countries that provides local projections and assessments of exposure to sea level rise and coastal flooding tabulated for many sub-national districts, down to cities and postal codes in the U.S. Exposure assessments always include land and population, and in the U.S. extend to over 100 demographic, economic, infrastructure and environmental variables using data drawn mainly from federal sources, including NOAA, USGS, FEMA, DOT, DOE, DOI, EPA, FCC and the Census.This web tool was highlighted at the launch of The White House's Climate Data Initiative in March 2014. Climate Central's original Surging Seas was featured on NBC, CBS, and PBS U.S. national news, the cover of The New York Times, in hundreds of other stories, and in testimony for the U.S. Senate. The Atlantic Cities named it the most important map of 2012. Both the Risk Zone map and the Risk Finder are grounded in peer-reviewed science.Back to topMethods and QualifiersThis map is based on analysis of digital elevation models mosaicked together for near-total coverage of the global coast. Details and sources for U.S. and international data are below. Elevations are transformed so they are expressed relative to local high tide lines (Mean Higher High Water, or MHHW). A simple elevation threshold-based “bathtub method” is then applied to determine areas below different water levels, relative to MHHW. Within the U.S., areas below the selected water level but apparently not connected to the ocean at that level are shown in a stippled green (as opposed to solid blue) on the map. Outside the U.S., due to data quality issues and data limitations, all areas below the selected level are shown as solid blue, unless separated from the ocean by a ridge at least 20 meters (66 feet) above MHHW, in which case they are shown as not affected (no blue).Areas using lidar-based elevation data: U.S. coastal states except AlaskaElevation data used for parts of this map within the U.S. come almost entirely from ~5-meter horizontal resolution digital elevation models curated and distributed by NOAA in its Coastal Lidar collection, derived from high-accuracy laser-rangefinding measurements. The same data are used in NOAA’s Sea Level Rise Viewer. (High-resolution elevation data for Louisiana, southeast Virginia, and limited other areas comes from the U.S. Geological Survey (USGS)). Areas using CoastalDEM™ elevation data: Antigua and Barbuda, Barbados, Corn Island (Nicaragua), Dominica, Dominican Republic, Grenada, Guyana, Haiti, Jamaica, Saint Kitts and Nevis, Saint Lucia, Saint Vincent and the Grenadines, San Blas (Panama), Suriname, The Bahamas, Trinidad and Tobago. CoastalDEM™ is a proprietary high-accuracy bare earth elevation dataset developed especially for low-lying coastal areas by Climate Central. Use our contact form to request more information.Warning for areas using other elevation data (all other areas)Areas of this map not listed above use elevation data on a roughly 90-meter horizontal resolution grid derived from NASA’s Shuttle Radar Topography Mission (SRTM). SRTM provides surface elevations, not bare earth elevations, causing it to commonly overestimate elevations, especially in areas with dense and tall buildings or vegetation. Therefore, the map under-portrays areas that could be submerged at each water level, and exposure is greater than shown (Kulp and Strauss, 2016). However, SRTM includes error in both directions, so some areas showing exposure may not be at risk.SRTM data do not cover latitudes farther north than 60 degrees or farther south than 56 degrees, meaning that sparsely populated parts of Arctic Circle nations are not mapped here, and may show visual artifacts.Areas of this map in Alaska use elevation data on a roughly 60-meter horizontal resolution grid supplied by the U.S. Geological Survey (USGS). This data is referenced to a vertical reference frame from 1929, based on historic sea levels, and with no established conversion to modern reference frames. The data also do not take into account subsequent land uplift and subsidence, widespread in the state. As a consequence, low confidence should be placed in Alaska map portions.Flood control structures (U.S.)Levees, walls, dams or other features may protect some areas, especially at lower elevations. Levees and other flood control structures are included in this map within but not outside of the U.S., due to poor and missing data. Within the U.S., data limitations, such as an incomplete inventory of levees, and a lack of levee height data, still make assessing protection difficult. For this map, levees are assumed high and strong enough for flood protection. However, it is important to note that only 8% of monitored levees in the U.S. are rated in “Acceptable” condition (ASCE). Also note that the map implicitly includes unmapped levees and their heights, if broad enough to be effectively captured directly by the elevation data.For more information on how Surging Seas incorporates levees and elevation data in Louisiana, view our Louisiana levees and DEMs methods PDF. For more information on how Surging Seas incorporates dams in Massachusetts, view the Surging Seas column of the web tools comparison matrix for Massachusetts.ErrorErrors or omissions in elevation or levee data may lead to areas being misclassified. Furthermore, this analysis does not account for future erosion, marsh migration, or construction. As is general best practice, local detail should be verified with a site visit. Sites located in zones below a given water level may or may not be subject to flooding at that level, and sites shown as isolated may or may not be be so. Areas may be connected to water via porous bedrock geology, and also may also be connected via channels, holes, or passages for drainage that the elevation data fails to or cannot pick up. In addition, sea level rise may cause problems even in isolated low zones during rainstorms by inhibiting drainage.ConnectivityAt any water height, there will be isolated, low-lying areas whose elevation falls below the water level, but are protected from coastal flooding by either man-made flood control structures (such as levees), or the natural topography of the surrounding land. In areas using lidar-based elevation data or CoastalDEM (see above), elevation data is accurate enough that non-connected areas can be clearly identified and treated separately in analysis (these areas are colored green on the map). In the U.S., levee data are complete enough to factor levees into determining connectivity as well.However, in other areas, elevation data is much less accurate, and noisy error often produces “speckled” artifacts in the flood maps, commonly in areas that should show complete inundation. Removing non-connected areas in these places could greatly underestimate the potential for flood exposure. For this reason, in these regions, the only areas removed from the map and excluded from analysis are separated from the ocean by a ridge of at least 20 meters (66 feet) above the local high tide line, according to the data, so coastal flooding would almost certainly be impossible (e.g., the Caspian Sea region).Back to topData LayersWater Level | Projections | Legend | Social Vulnerability | Population | Ethnicity | Income | Property | LandmarksWater LevelWater level means feet or meters above the local high tide line (“Mean Higher High Water”) instead of standard elevation. Methods described above explain how each map is generated based on a selected water level. Water can reach different levels in different time frames through combinations of sea level rise, tide and storm surge. Tide gauges shown on the map show related projections (see just below).The highest water levels on this map (10, 20 and 30 meters) provide reference points for possible flood risk from tsunamis, in regions prone to them.

This file contains the National Statistics Postcode Lookup (NSPL) for the United Kingdom as at August 2022 in Comma Separated Variable (CSV) and ASCII text (TXT) formats. To download the zip file click the Download button. The NSPL relates both current and terminated postcodes to a range of current statutory geographies via ‘best-fit’ allocation from the 2021 Census Output Areas (national parks and Workplace Zones are exempt from ‘best-fit’ and use ‘exact-fit’ allocations) for England and Wales. Scotland and Northern Ireland has the 2011 Census Output AreasIt supports the production of area based statistics from postcoded data. The NSPL is produced by ONS Geography, who provide geographic support to the Office for National Statistics (ONS) and geographic services used by other organisations. The NSPL is issued quarterly. (File size - 184 MB).

File List Individuals.csv (MD5: ): Height, condition, location of individual saguaros at plot X NurseInfo.csv (MD5: ): Nurse plant information at plot X Species.csv (MD5: ): Summary of plant codes and nomenclature of species detected Boundaries.shp (MD5: ): Layer depicting plot boundaries PlotX1908.shp: Layer depicting saguaros surveyed in 1908 that are within plot X (see ShpFiles.zip)

    All *.shp files are provided as the .zip file ShpFiles.zip (MD5: ); all *.csv files can be downloaded at once using CsvFiles.zip (MD5: ).
  Description
    The saguaro cactus (Carnegiea gigantea) is one of the most iconic perennials in the Sonoran Desert. The ecological importance of this species has motivated studies that explored its physiological adaptations to deserts, factors controlling its recruitment and distribution, and changes in its population density and extent over time. The population of saguaros on Tumamoc Hill (Tucson, Arizona) is one of the best studied. Saguaros on and nearby Tumamoc Hill were mapped in 1908, and in 1964 R. M. Turner and J. R. Hastings established four 250-m wide plots within the original census area. Plots were established on the north, south, east, and west-facing slopes of Tumamoc Hill, and each plot extends from the top to the base of the hill. Plots were resurveyed in 1970, 1987, 1993, and between 2010 and 2012. In this data paper, we present all information associated with this monitoring program, which includes digital versions of Spalding’s original 1908 saguaro map as well as information regarding individual saguaros located in each of the four plots. Collected data include plant height, number of branches, and plant condition, as well as plant location. Starting in 1993, we also noted the identity and condition of plant species growing in close proximity to each saguaro. The archived data set described here contains information pertaining to >5800 saguaros.
    Past analyses of these data include reconstructions of regeneration patterns from observed age structures and the determination of the average height-specific growth rates for plants on each slope. The findings from these studies have broadened our understanding of the relationship between saguaro regeneration patterns and climate. These data have also provided pivotal information regarding regional trends of saguaro populations throughout the Sonoran Desert. As a group, the Tumamoc Hill censuses constitute one of the longest spatially explicit monitoring efforts for a single species in the world. They provide an observational baseline for future comparisons relating individual growth and population demographics to rising CO2 levels, climate change, vegetation change, or changes in other biotic or abiotic factors.  

 Key words: Carnegiea gigantea; long-term monitoring; permanent plots; population dynamics; saguaro cactus; Sonoran Desert; spatially explicit data.

Frontier and Remote Area (FAR) codes provide a statistically-based, nationally-consistent, and adjustable definition of territory in the U.S. characterized by low population density and high geographic remoteness. To assist in providing policy-relevant information about conditions in sparsely settled, remote areas of the U.S. to public officials, researchers, and the general public, ERS has developed ZIP-code-level frontier and remote (FAR) area codes. The aim is not to provide a single definition. Instead, it is to meet the demand for a delineation that is both geographically detailed and adjustable within reasonable ranges, in order to be usefully applied in diverse research and policy contexts. This initial set, based on urban-rural data from the 2000 decennial census, provides four separate FAR definition levels, ranging from one that is relatively inclusive (18 million FAR residents) to one that is more restrictive (4.8 million FAR residents).

This table shows the 2021 population and dwelling counts for reported forward sortation areas.

The 2019 cartographic boundary shapefiles are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2010 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined as a result of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division (MCD) or incorporated place boundaries in some states and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2010 Census, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area.

Estimated noise levels in five Canadian cities were produced by a national team of researchers at the University of Montreal, Ryerson University, Dalhousie University, University of Toronto, the University of British Columbia, Public Health Ontario, the Montreal Regional Department of Public Health and the Department of Public Health of Monteregie, Longueuil. Two approaches were used: land use regression (LUR) and random forest (RF) models. Geographic predictor variables (e.g., proximity to airports, railways and traffic, population density, vegetation, etc.) around noise monitoring locations are used in both approaches to predict the measured level of noise. Results for each method are included in the dataset, although the research team notes that the RF model performed better than the LUR model. See details in the Supporting Documentation. Estimates are available for Vancouver (circa 2003), Toronto (circa 2016-2018), Montreal (circa 2010-2014, Longueuil (circa 2017), and Halifax (circa 2010). The research team used the model results to produce noise level estimates for postal code locations in each city. CANUE staff linked the estimates to annual postal code files from 1991 to 2019. IMPORTANT NOTE: The researchers report that estimated levels best represent the spatial pattern of noise within each city, rather than an accurate measure that would be suitable for analysis of recommended noise level thresholds. Also, the estimated levels represent different time periods in each city. As such, data users should consider using categories of exposure based on the data, as well as the estimated levels provided. Data users should also consider the time periods of the city-specific model results and how these relate to their study approach.

The 2013 Rural-Urban Continuum Codes form a classification scheme that distinguishes metropolitan counties by the population size of their metro area, and nonmetropolitan counties by degree of urbanization and adjacency to a metro area. The official Office of Management and Budget (OMB) metro and nonmetro categories have been subdivided into three metro and six nonmetro categories. Each county in the U.S. is assigned one of the 9 codes. This scheme allows researchers to break county data into finer residential groups, beyond metro and nonmetro, particularly for the analysis of trends in nonmetro areas that are related to population density and metro influence. The Rural-Urban Continuum Codes were originally developed in 1974. They have been updated each decennial since (1983, 1993, 2003, 2013), and slightly revised in 1988. Note that the 2013 Rural-Urban Continuum Codes are not directly comparable with the codes prior to 2000 because of the new methodology used in developing the 2000 metropolitan areas. See the Documentation for details and a map of the codes. An update of the Rural-Urban Continuum Codes is planned for mid-2023.

This resource is a member of a series. The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2020 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined as a result of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division (MCD) or incorporated place boundaries in some States and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2010 Census, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area.

This dataset contains all the data and code needed to reproduce the analyses in the manuscript: Penn, H. J., & Read, Q. D. (2023). Stem borer herbivory dependent on interactions of sugarcane variety, associated traits, and presence of prior borer damage. Pest Management Science. https://doi.org/10.1002/ps.7843 Included are two .Rmd notebooks containing all code required to reproduce the analyses in the manuscript, two .html file of rendered notebook output, three .csv data files that are loaded and analyzed, and a .zip file of intermediate R objects that are generated during the model fitting and variable selection process. Notebook files 01_boring_analysis.Rmd: This RMarkdown notebook contains R code to read and process the raw data, create exploratory data visualizations and tables, fit a Bayesian generalized linear mixed model, extract output from the statistical model, and create graphs and tables summarizing the model output including marginal means for different varieties and contrasts between crop years. 02_trait_covariate_analysis.Rmd: This RMarkdown notebook contains R code to read raw variety-level trait data, perform feature selection based on correlations between traits, fit another generalized linear mixed model using traits as predictors, and create graphs and tables from that model output including marginal means by categorical trait and marginal trends by continuous trait. HTML files These HTML files contain the rendered output of the two RMarkdown notebooks. They were generated by Quentin Read on 2023-08-30 and 2023-08-15. 01_boring_analysis.html 02_trait_covariate_analysis.html CSV data files These files contain the raw data. To recreate the notebook output the CSV files should be at the file path project/data/ relative to where the notebook is run. Columns are described below. BoredInternodes_26April2022_no format.csv: primary data file with sugarcane borer (SCB) damage Columns A-C are the year, date, and location. All location values are the same. Column D identifies which experiment the data point was collected from. Column E, Stubble, indicates the crop year (plant cane or first stubble) Column F indicates the variety Column G indicates the plot (integer ID) Column H indicates the stalk within each plot (integer ID) Column I, # Internodes, indicates how many internodes were on the stalk Columns J-AM are numbered 1-30 and indicate whether SCB damage was observed on that internode (0 if no, 1 if yes, blank cell if that internode was not present on the stalk) Column AN indicates the experimental treatment for those rows that are part of a manipulative experiment Column AO contains notes variety_lookup.csv: summary information for the 16 varieties analyzed in this study Column A is the variety name Column B is the total number of stalks assessed for SCB damage for that variety across all years Column C is the number of years that variety is present in the data Column D, Stubble, indicates which crop years were sampled for that variety ("PC" if only plant cane, "PC, 1S" if there are data for both plant cane and first stubble crop years) Column E, SCB resistance, is a categorical designation with four values: susceptible, moderately susceptible, moderately resistant, resistant Column F is the literature reference for the SCB resistance value Select_variety_traits_12Dec2022.csv: variety-level traits for the 16 varieties analyzed in this study Column A is the variety name Column B is the SCB resistance designation as an integer Column C is the categorical SCB resistance designation (see above) Columns D-I are continuous traits from year 1 (plant cane), including sugar (Mg/ha), biomass or aboveground cane production (Mg/ha), TRS or theoretically recoverable sugar (g/kg), stalk weight of individual stalks (kg), stalk population density (stalks/ha), and fiber content of stalk (percent). Columns J-O are the same continuous traits from year 2 (first stubble) Columns P-V are categorical traits (in some cases continuous traits binned into categories): maturity timing, amount of stalk wax, amount of leaf sheath wax, amount of leaf sheath hair, tightness of leaf sheath, whether leaf sheath becomes necrotic with age, and amount of collar hair. ZIP file of intermediate R objects To recreate the notebook output without having to run computationally intensive steps, unzip the archive. The fitted model objects should be at the file path project/ relative to where the notebook is run. intermediate_R_objects.zip: This file contains intermediate R objects that are generated during the model fitting and variable selection process. You may use the R objects in the .zip file if you would like to reproduce final output including figures and tables without having to refit the computationally intensive statistical models. binom_fit_intxns_updated_only5yrs.rds: fitted brms model object for the main statistical model binom_fit_reduced.rds: fitted brms model object for the trait covariate analysis marginal_trends.RData: calculated values of the estimated marginal trends with respect to year and previous damage marginal_trend_trs.rds: calculated values of the estimated marginal trend with respect to TRS marginal_trend_fib.rds: calculated values of the estimated marginal trend with respect to fiber content Resources in this dataset:Resource Title: Sugarcane borer damage data by internode, 1993-2021. File Name: BoredInternodes_26April2022_no format.csvResource Title: Summary information for the 16 sugarcane varieties analyzed. File Name: variety_lookup.csvResource Title: Variety-level traits for the 16 sugarcane varieties analyzed. File Name: Select_variety_traits_12Dec2022.csvResource Title: RMarkdown notebook 2: trait covariate analysis. File Name: 02_trait_covariate_analysis.RmdResource Title: Rendered HTML output of notebook 2. File Name: 02_trait_covariate_analysis.htmlResource Title: RMarkdown notebook 1: main analysis. File Name: 01_boring_analysis.RmdResource Title: Rendered HTML output of notebook 1. File Name: 01_boring_analysis.htmlResource Title: Intermediate R objects. File Name: intermediate_R_objects.zip